A new methodology for determining accident and injury contributing factors, and its application to road accidents on the Mumbai–Pune Expressway M Patel, S Kumar, S Balakumar, A Patel, M Bhuvanesh, M Painter, R Rajaraman, A M Hassan*, J Padmanaban** JP Research India Pvt. Ltd., Pune, Maharashtra, India *Former Research Fellow - Birmingham University, UK **JP Research Inc. 1975 W. El Camino Real, Suite 300, Mountain View, California 94040, USA Abstract - Road accidents are typically analyzed to address influences of human, vehicle, and environmental (primarily infrastructure) factors. A new methodology, based on a “Venn diagram” analysis, gives a broader perspective on the probable factors, and combinations of factors, contributing both to the occurrence of a crash and to sustaining injuries in that crash. The methodology was applied to 214 accidents on the Mumbai–Pune expressway. Factors contributing to accidents and injuries were addressed. The major human factors influencing accidents on this roadway were speeding (30%) and falling asleep (29%), while injuries were primarily due to lack of seat belt use (46%). The leading infrastructure factor for injuries was impact with a roadside manmade structure (28%), and the main vehicle factor for injuries was passenger compartment intrusion (73%). This methodology can help identify effective vehicle and infrastructure-related solutions for preventing accidents and mitigating injuries in India. INTRODUCTION The World Health Organization (WHO), in its Global Status Report on Road Safety 2013, observes that road traffic injuries are “the leading cause of death for young people aged 15-29” worldwide, and that, while many countries have taken steps to reduce fatalities from road traffic accidents, the total “remains unacceptably high at 1.24 million per year” [1]. To find effective solutions to this problem, an in-depth understanding of the problem is essential. Given the complexity of crash events and their causes, this is often a case of “easier said than done.” The first requirement, of course, is good data on real world crashes. The second is a means of using the data to understand what happens in these crashes and how both the crash events and their injury consequences could best be avoided. The focus of this study was development and application of a methodology to address this second requirement. Background The traditional wisdom regarding road accidents is that driver error is generally the root cause. In a comprehensive review of various approaches for using crash data to create safer road conditions, Stigson et al. [2] point out that, since 1980 the focus has been on the three factors that contribute to an accident: human, vehicle and road infrastructure/environment and their interactions. As that paper succinctly summarizes, early attempts to look at causation tended to link vehicle and environmental factors to the human factor, with the result that drivers and other road users were identified as “the sole or a contributory factor in approximately 95% of all crashes”. Not surprisingly, such a human factors-centered approach fails to address the vehicular and infrastructural problems that are equally significant in contributing to an accident, for an accident is not a singular event but a “dynamic system” [2]. In “Risk Management in a Dynamic Society: A Modelling Problem”, Rasmussen examined the causal foundation of hazardous industrial and transport accidents and rejected the idea of looking at separate elements in isolation in favor of considering the dynamic combination of all possible paths to and causes of failures [3]. That paper notes that while “it is often concluded in accident reviews that ‘human error’ is a determining factor … multiple contributing errors and faults are normally found”. Stigson et al. brings that point back to road accidents by applying one year of real-world fatal crash data to an analysis of the Swedish Road Administration (SRA) model for a safe transport system. The SRA model employs a Venn diagram approach and includes interactions between road users, vehicles
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A new methodology for determining accident and injury contributing
factors, and its application to road accidents
on the Mumbai–Pune Expressway
M Patel, S Kumar, S Balakumar, A Patel, M Bhuvanesh, M Painter,
R Rajaraman, A M Hassan*, J Padmanaban**
JP Research India Pvt. Ltd., Pune, Maharashtra, India
*Former Research Fellow - Birmingham University, UK
**JP Research Inc. 1975 W. El Camino Real, Suite 300, Mountain View, California 94040, USA
Abstract - Road accidents are typically analyzed to address influences of human, vehicle, and environmental (primarily
infrastructure) factors. A new methodology, based on a “Venn diagram” analysis, gives a broader perspective on the
probable factors, and combinations of factors, contributing both to the occurrence of a crash and to sustaining injuries in that
crash. The methodology was applied to 214 accidents on the Mumbai–Pune expressway. Factors contributing to accidents
and injuries were addressed. The major human factors influencing accidents on this roadway were speeding (30%) and
falling asleep (29%), while injuries were primarily due to lack of seat belt use (46%). The leading infrastructure factor for
injuries was impact with a roadside manmade structure (28%), and the main vehicle factor for injuries was passenger
compartment intrusion (73%). This methodology can help identify effective vehicle and infrastructure-related solutions for
preventing accidents and mitigating injuries in India.
INTRODUCTION
The World Health Organization (WHO), in its Global Status Report on Road Safety 2013, observes
that road traffic injuries are “the leading cause of death for young people aged 15-29” worldwide, and
that, while many countries have taken steps to reduce fatalities from road traffic accidents, the total
“remains unacceptably high at 1.24 million per year” [1].
To find effective solutions to this problem, an in-depth understanding of the problem is essential.
Given the complexity of crash events and their causes, this is often a case of “easier said than done.”
The first requirement, of course, is good data on real world crashes. The second is a means of using
the data to understand what happens in these crashes and how both the crash events and their injury
consequences could best be avoided. The focus of this study was development and application of a
methodology to address this second requirement.
Background
The traditional wisdom regarding road accidents is that driver error is generally the root cause. In a
comprehensive review of various approaches for using crash data to create safer road conditions,
Stigson et al. [2] point out that, since 1980 the focus has been on the three factors that contribute to an
accident: human, vehicle and road infrastructure/environment and their interactions. As that paper
succinctly summarizes, early attempts to look at causation tended to link vehicle and environmental
factors to the human factor, with the result that drivers and other road users were identified as “the
sole or a contributory factor in approximately 95% of all crashes”.
Not surprisingly, such a human factors-centered approach fails to address the vehicular and
infrastructural problems that are equally significant in contributing to an accident, for an accident is
not a singular event but a “dynamic system” [2]. In “Risk Management in a Dynamic Society: A
Modelling Problem”, Rasmussen examined the causal foundation of hazardous industrial and
transport accidents and rejected the idea of looking at separate elements in isolation in favor of
considering the dynamic combination of all possible paths to and causes of failures [3]. That paper
notes that while “it is often concluded in accident reviews that ‘human error’ is a determining factor
… multiple contributing errors and faults are normally found”.
Stigson et al. brings that point back to road accidents by applying one year of real-world fatal crash
data to an analysis of the Swedish Road Administration (SRA) model for a safe transport system. The
SRA model employs a Venn diagram approach and includes interactions between road users, vehicles
and “the road” (that is, the road environment, including infrastructure) — essentially all the factors
that together form the road transport system. The Stigson paper found that 93% of the fatal crashes in
that study were classifiable using the SRA model, and that, “of the three components, the road was
the one that was most often linked to a fatal outcome” [2].
Approach
For the current study, a Venn diagram approach was applied to a crash investigation of the Mumbai–
Pune Expressway, in India, to determine the contributing factors for accidents occurring on the
expressway. Implementing the SRA model to Indian conditions posed some difficulties that required
a modified approach. For example, there is no set benchmark for ideal conditions (required by the
SRA model). This made it impossible to correlate the factors based on their ratings, as had been done
by Stigson et al. for the Swedish crash study. The Stigson paper reports correlations based on the
European New Car Assessment Program (EuroNCAP) ratings for cars and European Road
Assessment Program Road Protection Score (EuroRAP RPS) ratings for roads.
In the absence of such standard rating systems, the SRA model needed to be refined to reflect the
Indian conditions. The new method was then tested by application to all accidents occurring on the
Mumbai–Pune Expressway over a period of 12 months. Like the SRA model, this method was used to
help determine the contributing factors leading to each accident and, separately, to injuries sustained
in each accident. This new methodology, developed from the SRA model, has proven to be useful not
only for identifying contributing factors but also for ranking them based on the number of accidents
these factors have influenced. This ranking is to help policy makers, decision makers and road safety
stakeholders in planning cost effective road safety investments using data-driven road safety
strategies.
This paper gives details of the contributing factors methodology, its application to crashes, and the
results and conclusions from the examination of road accidents on the Mumbai–Pune Expressway.
METHODOLOGY
The study included 214 accidents that occurred on the Mumbai Pune Expressway from October 2012
to October 2013. The accidents are part of an ongoing in-depth investigation under the RASSI (Road
Accident Sampling System–India) initiative, a database development effort supported by a
consortium of automobile original equipment manufacturers and JP Research India [4]. Appendices A
and B present some of the information captured and coded as part of detailed case investigations on
Indian roads.
As illustrated in Table 1, two accidents with the same accident type can have very different injury
outcomes. In Case 1, the driver slept and went off-road on his left. The car was lightly damaged and
the driver, who was belted, walked away with no major injuries. In Case 2, the driver of a similar car
slept and went off-road, but to the right side into the median space. This car impacted a concrete
barrier. The car experienced severe intrusions and the unbelted driver was fatal. In both circumstances
the causal scenario is the same: a sleepy driver, but the outcomes are drastically different. In order to
address this disparity, the accidents were analyzed to determine the contributing factors that led to
each accident and, separately, to the resulting injuries. Analyzing the accidents separately for accident
causation and injury causation gives a broader understanding of each accident.
Establishing a baseline
In keeping with the structure set up for the SRA, certain conditions were assumed to be the “ideal
conditions”, not meeting which would be considered a failure of that specific factor (human, vehicle
or infrastructure). These are listed in brief in Table 2. Keeping the ideal as the baseline, each accident
was coded for accident causation factors and injury causation factors.
Table 1. Example cases showing different injury outcomes from the same triggering factor
Points of comparison Case 1 Case 2
Scene photos
Taken along the direction
of vehicle’s travel
Vehicle photos
Damages sustained by the
vehicle
Injury severity No injury Fatal
Contributing factors
Leading to an accident Sleepy driver
Sleepy driver
Narrow shoulder width
Contributing factors
Leading to an injury
Not applicable
(No injury)
Manmade concrete barrier
Seatbelt not used by occupants
Passenger compartment intrusions
Table 2. Ideal conditions assumed for coding accident and injury causation
Category Accident ideals Injury ideals
Human
Sober/vigilant
Adheres to traffic rules
Uses available safety systems (e.g., side/rear mirrors, lights
as appropriate to conditions)
Proper loading and securing
of loads
Uses available safety
systems (e.g., seat belts and
helmets)
Vehicle
Safe-drivable condition (e.g.,
good tires, brakes, steering)
No room for overloading
(occupants and cargo)
No passenger compartment
intrusion
Seat belts available in all
seating positions
Infrastructure
Good surface condition (e.g.,
dry, even, unbroken)
Proper signage/warnings (e.g.,
curves, mergers)
Sufficient shoulder width
Good layout/traffic flow
Visibility not obstructed
No rigid barrier without
proper impact attenuators
“Forgiving” features on
roadside and median where
needed (e.g., steep slope or
drop-off)
Accident causation: baseline
For accident avoidance, an ideal condition as a starting point for examining the “human factor”
influences is defined as the occupant/cyclist/pedestrian is sober and alert, obeys road regulations and
has properly used the available safety systems (mirrors, etc.), as outlined in Table 2. Any variation
from this ideal is noted in the causal analysis. A vehicle is defined as ideal when the vehicle is in a
safe drivable condition and does not allow overloading of occupants and cargo that affects the
dynamics of vehicle. Road conditions are considered ideal when the road section is in good condition
and has proper signage, sufficient shoulder widths, intuitive road layout and function (for turns,
merging, etc.), and good visibility. If any of these ideal conditions are not met, the failure is recorded.
Injury causation: baseline
For injury avoidance, an ideal human condition exists when occupants/cyclists/pedestrians have
properly used the available safety systems (seat belts, helmets, etc.), the vehicle is not overloaded
(includes passenger loads) and any non-human loads are properly fastened. Ideal vehicle conditions
exist when the vehicle has seat belts available for all its seating positions and suffers no passenger
compartment intrusion in the accident. Ideal road conditions exist when there are no rigid barriers
(including trees) or other dangerous features, such as steep drop offs, rocky outcrops, etc., alongside
the roadway or median. If rigid barriers/dangerous conditions do exist, they should be mitigated by
impact attenuators or by structures that can afford sufficient protection to keep vehicles safely on the
road while still being forgiving enough to avoid creating even more dangerous impact situations than
the ones they are protecting against.
Example: baseline applied
As an example of how this works, consider Case 2 from Table 1. In this instance, the contributing
factors that led to the accident are human factors alone: driver sleepy and not vigilant (just as in Case
1). However, the contributing factors that led to the fatal injuries are more involved:
Human - Driver not belted
Vehicle - Passenger compartment intrusion
Infrastructure - Absence of impact attenuators before a rigid barrier
Each accident in this study was analyzed against the accident and injury baselines in a fashion similar
to that shown in Table 1. The factors were then ranked. For accident causation, this ranking is based
on the number of accidents a factor has influenced. For injury causation, the ranking is based on the
number of injury occurrences that specific factor has influenced.
Study area
The Mumbai–Pune Expressway is a 94-kilometer, controlled-access highway that connects Mumbai,
the commercial capital of India, to the neighboring city of Pune, an educational and information
technology hub of India. It is a six-lane roadway with a speed limit of 80 km/h along most of its
stretch. Two-wheelers, three-wheelers and pedestrians are not permitted to use most parts of the
expressway and non-motorized vehicles are not permitted for the whole stretch. Common vehicle
types plying the expressway are cars, trucks and buses.
Data analysis
The methodology study consisted of analysis of contributing factors for 214 accidents (irrespective of
injury) that occurred on the Mumbai–Pune Expressway over 12 consecutive months. A second
analysis was conducted for those 68 accidents that resulted in a fatal or serious injury.
Injury severity definitions
Figure 1 shows the distribution of accidents by the highest level of injury (severity) sustained by any
involved party. The definitions for each level of severity are as follows:
Fatal Injury: An accident involving at least one fatality. Any victim who dies within 30 days of the
accident as a result of the injuries due to the accident is counted as a fatality.
Serious Injury: An accident with no fatalities, but with at least one or more victims hospitalized for
more than 24 hours.
Minor Injury: An accident in which victims suffer minor injuries which are treated on-scene (first
aid) or in a hospital as an outpatient.
No Injury: An accident in which no injuries are sustained by any of the involved persons.
Usually only vehicle damage occurs as a result of the accident.
Figure 1. Distribution of accidents by highest injury severity
Fatal17%
Serious14%
Minor24%
No Injury43%
Unknown2%
Factors influencing occurrence of accidents (214 accidents)
A distribution by contributing factors (human/vehicle/infrastructure) for the accidents analyzed is
shown in the Venn diagram presented as Figure 2. This diagram shows that human factors alone
(57%) had the highest influence on the occurrence of accidents, followed by the combination of
human and infrastructure factors (22.5%) and vehicle factors alone (16.5%).
Figure 2. Distribution of accidents by contributing factors influencing accident occurrence
Figure 3. Distribution of fatal/serious injury accidents by contributing factors
influencing injury occurrence
57%
2%
0%
22.5%
Human (81.5%)
16.5% 1% 1%
10%
21%
12% 7%
Human (50%)
28% 3% 19%
Factors influencing occurrence of injuries (68 fatal/serious accidents)
Of the 214 accidents, 68 accidents involved fatal or serious injury to at least one occupant or
pedestrian. The distribution by contributing factors (human/vehicle/infrastructure) is shown in the
Venn diagram presented as Figure 3. This diagram shows that vehicle factors alone (28%) had the
greatest influence on a fatal/serious injury outcome, followed by a combination of human and vehicle
factors (21%) and combination of vehicle and infrastructure factors (19%).
When the overlapping combinations are considered, infrastructure factors, which were not so
pronounced as a stand-alone (showing only a 3% influence) become more evident (41%).
FINDINGS
The focus of this paper is on the application of a new methodology modified for India, and the
findings presented here are offered as demonstration of types of results obtained using this new
methodology. For more details on the findings themselves, see the Mumbai–Pune Expressway Road
Accident Study [5].
Accident occurrence
Accident causal factors were analyzed using the new methodology for all 214 accidents, as described
under Methodology. The findings are presented by contributing factor type (human, vehicle, or
infrastructure). Please note that more than one factor can influence an accident; hence, the sum of
percentage influence may not be equal to sum of factors influencing accidents.
Human factors
Table 3 shows the top five contributing human factors that influenced accidents. Speeding and fatigue
are the main contributors. Other contributing factors include following too closely (4%), parked
vehicle on road (4%), wrong usage of lanes (3%), parked vehicle off road (2%), overtaking from left
of vehicle (2%), illegal road usage (2%), driving under the influence of alcohol or drugs (1%) and
dangerous pedestrian behavior on roadway (1%).
Table 3. Contributing human factors influencing accident occurrence
Contributing human factors
(Accident occurrence) Number of accidents % Influenced